import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE__		:       List[Any]       						=       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:       Optional[Any]       						=       {
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}

class    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  'deta'
  lowercase_								=  {
      'hidden_size': 'd_model',
      'num_attention_heads': 'encoder_attention_heads',
  }




  def __init__(   self  :						Union[str, Any]  ,			a_  :						Dict=None  ,			a_  :						Tuple=900  ,			a_  :						Any=2048  ,			a_  :						List[str]=6  ,			a_  :						int=2048  ,			a_  :						Union[str, Any]=8  ,			a_  :						List[Any]=6  ,			a_  :						List[Any]=1024  ,			a_  :						Union[str, Any]=8  ,			a_  :						List[Any]=0.0  ,			a_  :						List[Any]=True  ,			a_  :						str="relu"  ,			a_  :						Any=256  ,			a_  :						Optional[Any]=0.1  ,			a_  :						Dict=0.0  ,			a_  :						Union[str, Any]=0.0  ,			a_  :						Optional[int]=0.02  ,			a_  :						Optional[Any]=1.0  ,			a_  :						Dict=True  ,			a_  :						int=False  ,			a_  :						List[str]="sine"  ,			a_  :						Dict=5  ,			a_  :						Tuple=4  ,			a_  :						Union[str, Any]=4  ,			a_  :						Dict=True  ,			a_  :						str=300  ,			a_  :						Union[str, Any]=True  ,			a_  :						List[Any]=True  ,			a_  :						List[Any]=1  ,			a_  :						List[str]=5  ,			a_  :						Optional[int]=2  ,			a_  :						List[str]=1  ,			a_  :						Dict=1  ,			a_  :						List[str]=5  ,			a_  :						List[Any]=2  ,			a_  :						Union[str, Any]=0.1  ,			a_  :						int=0.25  ,			**a_  :						List[str]  ,			)->							int:




       """simple docstring"""
       if backbone_config is None:
            logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'					)
            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		CONFIG_MAPPING['resnet'](out_features=['stage2', 'stage3', 'stage4']					)
       else:
            if isinstance(a_  ,			a_					):
                 SCREAMING_SNAKE_CASE__					:					Optional[int]      =		backbone_config.pop('model_type'					)
                 SCREAMING_SNAKE_CASE__					:					Dict      =		CONFIG_MAPPING[backbone_model_type]
                 SCREAMING_SNAKE_CASE__					:					List[Any]      =		config_class.from_dict(a_					)

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		backbone_config
       SCREAMING_SNAKE_CASE__					:					Any      =		num_queries
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		max_position_embeddings
       SCREAMING_SNAKE_CASE__					:					Tuple      =		d_model
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		encoder_ffn_dim
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		encoder_layers
       SCREAMING_SNAKE_CASE__					:					str      =		encoder_attention_heads
       SCREAMING_SNAKE_CASE__					:					str      =		decoder_ffn_dim
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		decoder_layers
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		decoder_attention_heads
       SCREAMING_SNAKE_CASE__					:					str      =		dropout
       SCREAMING_SNAKE_CASE__					:					Dict      =		attention_dropout
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		activation_dropout
       SCREAMING_SNAKE_CASE__					:					int      =		activation_function
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		init_std
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		init_xavier_std
       SCREAMING_SNAKE_CASE__					:					str      =		encoder_layerdrop
       SCREAMING_SNAKE_CASE__					:					List[str]      =		auxiliary_loss
       SCREAMING_SNAKE_CASE__					:					Tuple      =		position_embedding_type
       # deformable attributes
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		num_feature_levels
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		encoder_n_points
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		decoder_n_points
       SCREAMING_SNAKE_CASE__					:					Any      =		two_stage
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		two_stage_num_proposals
       SCREAMING_SNAKE_CASE__					:					Any      =		with_box_refine
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		assign_first_stage
       if two_stage is True and with_box_refine is False:
            raise ValueError('If two_stage is True, with_box_refine must be True.'					)
       # Hungarian matcher
       SCREAMING_SNAKE_CASE__					:					Dict      =		class_cost
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		bbox_cost
       SCREAMING_SNAKE_CASE__					:					int      =		giou_cost
       # Loss coefficients
       SCREAMING_SNAKE_CASE__					:					Any      =		mask_loss_coefficient
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		dice_loss_coefficient
       SCREAMING_SNAKE_CASE__					:					int      =		bbox_loss_coefficient
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		giou_loss_coefficient
       SCREAMING_SNAKE_CASE__					:					str      =		eos_coefficient
       SCREAMING_SNAKE_CASE__					:					List[str]      =		focal_alpha
       super().__init__(is_encoder_decoder=a_  ,			**a_					)




  @property
  def 						__lowercase(   self  :						Optional[int]					)->							int:




       """simple docstring"""
       return self.encoder_attention_heads




  @property
  def 						__lowercase(   self  :						Optional[Any]					)->							int:




       """simple docstring"""
       return self.d_model




  def 						__lowercase(   self  :						Optional[int]					)->							int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					str      =		copy.deepcopy(self.__dict__					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.backbone_config.to_dict()
       SCREAMING_SNAKE_CASE__					:					Dict      =		self.__class__.model_type
       return output










"""simple docstring"""





from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)






